Abstract Operating staffs of a nuclear power plant (NPP) are responsible for returning the NPP to a stable state and alert authorities if there is the potential for offsite radiological consequences following an accident. An operator support tool (OST) using deep learning techniques and trained by data from dynamic probabilistic safety/risk assessment (DPSA/DPRA) is proposed to assist the NPP personnel in decision-making. The DPSA/DPRA methodology employs time-dependent branching conditions based on the evolving state of the NPP and accounts for complex hardware/process/software/human interactions to predict possible outcomes of the initiating event. A large number of scenarios generated from the DPSA/DPRA performed for a pressurized water reactor station blackout as a function of time were used to train the OST to predict possible offsite dose outcomes at 2-mile and 10-mile site boundaries for emergency response planning. The results show that the OST can predict offsite dose levels with greater than 90% accuracy.

中文翻译：

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使用动态事件树和深度学习进行核电厂严重事故管理的在线操作员支持工具

摘要 核电厂 (NPP) 的运行人员有责任将核电厂恢复到稳定状态，并在发生事故后可能出现厂外放射性后果时向当局发出警报。建议使用深度学习技术并通过动态概率安全/风险评估 (DPSA/DPRA) 数据训练的运营商支持工具 (OST) 来协助核电厂人员进行决策。DPSA/DPRA 方法采用基于 NPP 演变状态的时间相关分支条件，并考虑复杂的硬件/过程/软件/人机交互来预测起始事件的可能结果。使用 DPSA/DPRA 生成的大量场景作为时间的函数，用于对 OST 进行训练，以预测 2 英里和 10 英里站点边界处可能的场外剂量结果，以进行应急响应规划. 结果表明，OST 可以以超过 90% 的准确度预测场外剂量水平。